Labelled drug-related public expenditure in relation to gross domestic product (gdp) in Europe: A luxury good?

"Labelled drug-related public expenditure" is the direct expenditure explicitly labelled as related to illicit drugs by the general government of the state. As part of the reporting exercise corresponding to 2005, the European Monitoring Centre for Drugs and Drug Addiction's network of national focal points set up in the 27 European Union (EU) Member States, Norway, and the candidates countries to the EU, were requested to identify labelled drug-related public expenditure, at the country level. This was reported by 10 countries categorised according to the functions of government, amounting to a total of EUR 2.17 billion. Overall, the highest proportion of this total came within the government functions of Health (66%), and Public Order and Safety (POS) (20%). By country, the average share of GDP was 0.023% for Health, and 0.013% for POS. However, these shares varied considerably across countries, ranging from 0.00033% in Slovakia, up to 0.053% of GDP in Ireland in the case of Health, and from 0.003% in Portugal, to 0.02% in the UK, in the case of POS; almost a 161-fold difference between the highest and the lowest countries for Health, and a 6-fold difference for POS. Why do Ireland and the UK spend so much in Health and POS, or Slovakia and Portugal so little, in GDP terms? To respond to this question and to make a comprehensive assessment of drug-related public expenditure across countries, this study compared Health and POS spending and GDP in the 10 reporting countries. Results found suggest GDP to be a major determinant of the Health and POS drug-related public expenditures of a country. Labelled drug-related public expenditure showed a positive association with the GDP across the countries considered: r = 0.81 in the case of Health, and r = 0.91 for POS. The percentage change in Health and POS expenditures due to a one percent increase in GDP (the income elasticity of demand) was estimated to be 1.78% and 1.23% respectively. Being highly income elastic, Health and POS expenditures can be considered luxury goods; as a nation becomes wealthier it openly spends proportionately more on drug-related health and public order and safety interventions

Impact of population ageing on the cost of hospitalisations for cardiovascular disease: a population-based data linkage study

Background: Cardiovascular disease (CVD) is the most costly disease in Australia. Measuring the impact of ageing on its costs is needed for planning future healthcare budget. The aim of this study was to measure the impact of changes in population age structure in Western Australia (WA) on the costs of hospitalisation for CVD. Methods: All hospitalisation records for CVD occurring in WA in 1993/94 and 2003/04 inclusive were extracted from the WA Hospital Morbidity Data System (HMDS) via the WA Data Linkage System. Inflation adjusted hospitalisation costs using 2012 as the base year was assigned to all episodes of care using Australian Refined Diagnosis Related Group (AR-DRG) costing information. The component decomposition method was used to measure the contribution of ageing and other factors to the increase of hospitalisation costs for CVD. Results: Between 1993/94 and 2003/04, population ageing contributed 23% and 30% respectively of the increase in CVD hospitalisation costs for men and women. The impact of ageing on hospitalisation costs was far greater for chronic conditions than acute coronary syndrome (ACS) and stroke. Conclusions: Given the impact of ageing on hospitalisation costs, and the disparity between chronic and acute conditions, disease-specific factors should be considered in planning for future healthcare expenditure

Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity

In this study, Bayesian inference is developed for structural vector autoregressive models in which the structural parameters are identified via Markov-switching heteroskedasticity. In such a model, restrictions that are just-identifying in the homoskedastic case, become over-identifying and can be tested. A set of parametric restrictions is derived under which the structural matrix is globally or partially identified and a Savage-Dickey density ratio is used to assess the validity of the identification conditions. The latter is facilitated by analytical derivations that make the computations fast and numerical standard errors small. As an empirical example, monetary models are compared using heteroskedasticity as an additional device for identification. The empirical results support models with money in the interest rate reaction function.Comment: Keywords: Identification Through Heteroskedasticity, Bayesian Hypotheses Assessment, Markov-switching Models, Mixture Models, Regime Chang